In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Esti...In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.展开更多
Wind-power （WP） estimation is necessary for power system in several operations, which are as the optimal power flow between conventional units and wind farms, generators scheduling, and electricity market bidding. E...Wind-power （WP） estimation is necessary for power system in several operations, which are as the optimal power flow between conventional units and wind farms, generators scheduling, and electricity market bidding. Estimating the output power of a wind energy conversion unit （WEC） mainly bases on the incident wind speed at the unit site by using the power characteristic curve. In addition, several time-series models have been using in wind speed forecasting. These models are characterized with requiring a large set of data. In order to prevent from the wind speed measurement and the need of a precise wind turbine model, an novel method basing on neural network and the grey predictor model GM （1,1） is proposed. Though the method, the estimating model can be built only by using the experimental data, which are obtained from the WP system in laboratory. The effectiveness of the estimating model is confirmed by the simulation results.展开更多
On the basis of Zeng's theorehcal design, a coupled general circulation model(CGCM) is develO￣ with itscharacteristics different from other CGCMs such as the unified vertical coordinates and subtraction of the st...On the basis of Zeng's theorehcal design, a coupled general circulation model(CGCM) is develO￣ with itscharacteristics different from other CGCMs such as the unified vertical coordinates and subtraction of the standard stratification for both atmosphere and ocean, available energy consideration,and so on.The oceanic comPOnent is a free surface tropical Pacific Ocean GCM betWeen 30W and 30'S with horizontal grid spacing of ic in latitude and 2°in longitude,and with 14 vertical layers.The atmospheric component is a global GCM with low-resolution of 4°in lahtude and 5°in longitude,and tWo layers of equal mass in the verhcal between the surfaCe and 200 hFa.The atmospheric GCM includes comprehensive physical processes.The coupled model is subjected to seasonally-varying cycle.Several coupling experiments,ranging from straight forward coupling without flux correction to one with flux correchon,and to so-called predictor-corrector monthly coupling(PCMC),are conducted tO show the esistence and final controlling of the climate drift in the coupled system.After removing the climate drift with the PCMC SCheme,the coupled model is integrated for more than twenty years.The results show reasonable simulations of the anneal mean and its seasollal cycle of the atmospheric and￣ante circulahon.The model also ProduCeS the coherent intermnual variations of the climate system, manifesting the observed EI Nifio/Southern OSCillation(ENSO).展开更多
BACKGROUND Tens of millions of gastrointestinal endoscopic procedures are performed every year in China,but the quality varies significantly and related factors are complex.Individual endoscopist-and endoscopy divisio...BACKGROUND Tens of millions of gastrointestinal endoscopic procedures are performed every year in China,but the quality varies significantly and related factors are complex.Individual endoscopist-and endoscopy division-related factors may be useful to establish a model to measure and predict the quality of endoscopy.AIM To establish a model to measure and predict the quality of gastrointestinal endoscopic procedures in China's Mainland.METHODS Selected data on endoscopy experience,equipment,facility,qualification of endoscopists,and other relevant variables were collected from the National Database of Digestive Endoscopy of China.The multivariable logistic regression analysis was used to identify the potential predictive variables for occurrence of medical malpractice and patient disturbance.Linear and nonlinear regressions were used to establish models to predict incidence of endoscopic complications.RESULTS In 2012,gastroscopy/colonoscopy-related complications in China's Mainland included bleeding in 4,359 cases(0.02%)and perforation in 914(0.003%).Endoscopic-retrograde-cholangiopancreatography-related complications included severe acute pancreatitis in 593 cases(0.3%),bleeding in 2,151(1.10%),perforation in 257(0.13%)and biliary infection in 4,125(2.11%).Moreover,1,313(5.0%)endoscopists encountered with medical malpractice,and 5,243(20.0%)encountered with the disturbance from patients.The length of endoscopy experience,weekly working hours,weekly night shifts,annual vacation days and job satisfaction were predictors for the occurrence of medical malpractice and patient disturbance.However,the length of endoscopy experience and the ratio of endoscopists to nurses were not adequate to establish an effective predictive model for endoscopy complications.CONCLUSION The workload and job satisfaction of endoscopists are valuable predictors for medical malpractice or patient disturbance.More comprehensive data are needed to establish quality-predictive models for endoscopic complications.展开更多
Tuberculosis is one of the leading causes of morbidity and mortality globally. Although different strategies have been designed and implemented to combat it, it has continuously increased in the past five years, resul...Tuberculosis is one of the leading causes of morbidity and mortality globally. Although different strategies have been designed and implemented to combat it, it has continuously increased in the past five years, resulting in 10 million new cases and 1.6 million deaths. This study aims to estimate survival and predictors among tuberculosis patients on treatment in selected health centers in Addis Ababa, Ethiopia. The study employed a retrospective cohort design where data were collected by reviewing medical records of tuberculosis patients who were registered from May 2016 to May 2017 on treatment in 20 selected health centers in Addis Ababa. Independent predictors were identified, and the strength of association between dependent and independent predictors was determined using the Weibull regression model. Before computing Weibull regression analysis, Cox proportional assumption, model diagnosis, and fitness were checked. The hazard ratio was calculated to indicate the strength of association. Of 371 TB patients, about 136 (36.7%) died during the treatment period. Most TB deaths occurred during the intensive phase, and the overall estimated median survival time was 157 days. In the multivariable Weibull model, age (HR = 0.98), baseline weight (HR = 0.96, P = 0.03), tuberculosis treatment phase (continuation phase, HR = 0.48), and tuberculosis type (pulmonary negative TB, HR = 19.92) were found to be independent predictors of time to death of tuberculosis patients. Finally, the study concluded that the survival time to death of the patients is high. The health care providers should give special attention and follow up for pulmonary negative and underweight TB patients.展开更多
Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, th...Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.展开更多
目的基于抗环瓜氨酸肽抗体(抗CCP抗体)及类风湿因子(rheumatoid factor,RF)等构建列线图预测模型,用于预测类风湿关节炎(rheumatoid arthritis,RA)患者合并心血管疾病(cardiovascular diseases,CVD)的概率。方法选取2018年1月~2024年2...目的基于抗环瓜氨酸肽抗体(抗CCP抗体)及类风湿因子(rheumatoid factor,RF)等构建列线图预测模型,用于预测类风湿关节炎(rheumatoid arthritis,RA)患者合并心血管疾病(cardiovascular diseases,CVD)的概率。方法选取2018年1月~2024年2月郑州大学附属郑州中心医院收治的437例RA患者作为研究对象,搜集患者的临床资料,结合随访结果将其分为RA+CVD组(n=88)和RA组(n=349)。通过单因素以及多因素Logistic回归分析筛选RA患者发生CVD的危险因素,据此构建RA患者发生CVD的预测模型,并进行内部验证。结果多因素Logistic回归分析结果显示,年龄、病程、尿酸、C反应蛋白、是否有高血压及糖尿病、抗CCP抗体、RF是RA患者合并CVD的独立危险因素(P<0.05)。对所构建的模型进行内部验证。其训练集和验证集的曲线下面积(area under the area,AUC)分别为0.891(95%CI:0.851~0.930)和0.867(95%CI:0.790~0.944)。结论本研究所构建的预测模型预测及区分能力较好,对RA患者是否合并CVD具有较高的预测价值。展开更多
文摘In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.
文摘Wind-power （WP） estimation is necessary for power system in several operations, which are as the optimal power flow between conventional units and wind farms, generators scheduling, and electricity market bidding. Estimating the output power of a wind energy conversion unit （WEC） mainly bases on the incident wind speed at the unit site by using the power characteristic curve. In addition, several time-series models have been using in wind speed forecasting. These models are characterized with requiring a large set of data. In order to prevent from the wind speed measurement and the need of a precise wind turbine model, an novel method basing on neural network and the grey predictor model GM （1,1） is proposed. Though the method, the estimating model can be built only by using the experimental data, which are obtained from the WP system in laboratory. The effectiveness of the estimating model is confirmed by the simulation results.
文摘On the basis of Zeng's theorehcal design, a coupled general circulation model(CGCM) is develO￣ with itscharacteristics different from other CGCMs such as the unified vertical coordinates and subtraction of the standard stratification for both atmosphere and ocean, available energy consideration,and so on.The oceanic comPOnent is a free surface tropical Pacific Ocean GCM betWeen 30W and 30'S with horizontal grid spacing of ic in latitude and 2°in longitude,and with 14 vertical layers.The atmospheric component is a global GCM with low-resolution of 4°in lahtude and 5°in longitude,and tWo layers of equal mass in the verhcal between the surfaCe and 200 hFa.The atmospheric GCM includes comprehensive physical processes.The coupled model is subjected to seasonally-varying cycle.Several coupling experiments,ranging from straight forward coupling without flux correction to one with flux correchon,and to so-called predictor-corrector monthly coupling(PCMC),are conducted tO show the esistence and final controlling of the climate drift in the coupled system.After removing the climate drift with the PCMC SCheme,the coupled model is integrated for more than twenty years.The results show reasonable simulations of the anneal mean and its seasollal cycle of the atmospheric and￣ante circulahon.The model also ProduCeS the coherent intermnual variations of the climate system, manifesting the observed EI Nifio/Southern OSCillation(ENSO).
文摘BACKGROUND Tens of millions of gastrointestinal endoscopic procedures are performed every year in China,but the quality varies significantly and related factors are complex.Individual endoscopist-and endoscopy division-related factors may be useful to establish a model to measure and predict the quality of endoscopy.AIM To establish a model to measure and predict the quality of gastrointestinal endoscopic procedures in China's Mainland.METHODS Selected data on endoscopy experience,equipment,facility,qualification of endoscopists,and other relevant variables were collected from the National Database of Digestive Endoscopy of China.The multivariable logistic regression analysis was used to identify the potential predictive variables for occurrence of medical malpractice and patient disturbance.Linear and nonlinear regressions were used to establish models to predict incidence of endoscopic complications.RESULTS In 2012,gastroscopy/colonoscopy-related complications in China's Mainland included bleeding in 4,359 cases(0.02%)and perforation in 914(0.003%).Endoscopic-retrograde-cholangiopancreatography-related complications included severe acute pancreatitis in 593 cases(0.3%),bleeding in 2,151(1.10%),perforation in 257(0.13%)and biliary infection in 4,125(2.11%).Moreover,1,313(5.0%)endoscopists encountered with medical malpractice,and 5,243(20.0%)encountered with the disturbance from patients.The length of endoscopy experience,weekly working hours,weekly night shifts,annual vacation days and job satisfaction were predictors for the occurrence of medical malpractice and patient disturbance.However,the length of endoscopy experience and the ratio of endoscopists to nurses were not adequate to establish an effective predictive model for endoscopy complications.CONCLUSION The workload and job satisfaction of endoscopists are valuable predictors for medical malpractice or patient disturbance.More comprehensive data are needed to establish quality-predictive models for endoscopic complications.
文摘Tuberculosis is one of the leading causes of morbidity and mortality globally. Although different strategies have been designed and implemented to combat it, it has continuously increased in the past five years, resulting in 10 million new cases and 1.6 million deaths. This study aims to estimate survival and predictors among tuberculosis patients on treatment in selected health centers in Addis Ababa, Ethiopia. The study employed a retrospective cohort design where data were collected by reviewing medical records of tuberculosis patients who were registered from May 2016 to May 2017 on treatment in 20 selected health centers in Addis Ababa. Independent predictors were identified, and the strength of association between dependent and independent predictors was determined using the Weibull regression model. Before computing Weibull regression analysis, Cox proportional assumption, model diagnosis, and fitness were checked. The hazard ratio was calculated to indicate the strength of association. Of 371 TB patients, about 136 (36.7%) died during the treatment period. Most TB deaths occurred during the intensive phase, and the overall estimated median survival time was 157 days. In the multivariable Weibull model, age (HR = 0.98), baseline weight (HR = 0.96, P = 0.03), tuberculosis treatment phase (continuation phase, HR = 0.48), and tuberculosis type (pulmonary negative TB, HR = 19.92) were found to be independent predictors of time to death of tuberculosis patients. Finally, the study concluded that the survival time to death of the patients is high. The health care providers should give special attention and follow up for pulmonary negative and underweight TB patients.
文摘Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">in </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.
文摘目的基于抗环瓜氨酸肽抗体(抗CCP抗体)及类风湿因子(rheumatoid factor,RF)等构建列线图预测模型,用于预测类风湿关节炎(rheumatoid arthritis,RA)患者合并心血管疾病(cardiovascular diseases,CVD)的概率。方法选取2018年1月~2024年2月郑州大学附属郑州中心医院收治的437例RA患者作为研究对象,搜集患者的临床资料,结合随访结果将其分为RA+CVD组(n=88)和RA组(n=349)。通过单因素以及多因素Logistic回归分析筛选RA患者发生CVD的危险因素,据此构建RA患者发生CVD的预测模型,并进行内部验证。结果多因素Logistic回归分析结果显示,年龄、病程、尿酸、C反应蛋白、是否有高血压及糖尿病、抗CCP抗体、RF是RA患者合并CVD的独立危险因素(P<0.05)。对所构建的模型进行内部验证。其训练集和验证集的曲线下面积(area under the area,AUC)分别为0.891(95%CI:0.851~0.930)和0.867(95%CI:0.790~0.944)。结论本研究所构建的预测模型预测及区分能力较好,对RA患者是否合并CVD具有较高的预测价值。
